Technical documentationPersonality test

Bart Dekker MSc.Dr. Edwin van Thiel

Last updated on 01 April, 2020

INHOUDSOPGAVE INHOUDSOPGAVE

Inhoudsopgave1 Introduction 3

2 Method 32.1 Instruments used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

3 Exploration and selection of norm data 43.1 Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43.2 Sex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43.3 Level of education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.4 Nationality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.5 Labour market position . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.6 Work sector/industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73.7 Completion time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83.8 Response Variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83.9 Human response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

4 Analysis 104.1 Raw scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

4.1.1 Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104.1.2 Facets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

4.2 Correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164.2.1 Correlations between factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164.2.2 Correlations between facets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4.3 Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184.3.1 Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184.3.2 Facets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.4 Construct Validity: Factor Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194.4.1 Screeplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194.4.2 Principal Components Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

5 Norms 225.1 Labour force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225.2 Group differences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

5.2.1 Sex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235.2.2 Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245.2.3 Level of education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

6 Conclusion 26

7 References 27

2

2 METHOD

1 Introduction

This document provides insight into the psychometry of the Big Five personality test of 123test. This test,developed by 123test B.V., is an operationalisation of the Big Five personality theory.

The test measures the five main dimensions of personality and the 30 underlying facets. This makes it ascientific instrument, which, moreover, has a high degree of reliability. has a high validity and arepresentative and recently assembled norm group used.

Information on reliability, validity and norm groups are described in this document. Also discussed how thedimensions of this test vary as a function of level of education, gender and age.

2 Method

Since November 1, 2019, more than 500,000 responses of the Big Five Personality Test have been recorded onwww.123test.com. By analyzing the data of these anonymous respondents, we can form a good picture of thisinstrument.

2.1 Instruments used

The Big Five Personality Test is free to use at https://www.123test.com/personality-test/. The Dutchequivalent of this questionnaire can be found at https://www.123test.nl/persoonlijkheidstest/ and can alsobe used free of charge.

3

3 EXPLORATION AND SELECTION OF NORM DATA

3 Exploration and selection of norm data

In order to explore the gathered data, this chapter examines a number of background variables of therespondent in more detail. The selection criteria used for the final dataset are indicated for each component.

The complete dataset consists of 490.689 respondents. Based on cirteria such as age, sex, educational level,nationality, labour market position, completion time and response variation, the final subset is made on whichanalyses are done and with which the final norm is calculated.

3.1 Age

An age group of 18 to 67 years has been chosen, because this group best represents the working population ofthe Western world.

Age in years

Fre

quen

cy

10 20 30 40 50 60 70 80

010

000

2000

030

000

3.2 Sex

The sex of all respondents is known because this background question was mandatory. Striking is the highernumber of women who completed the test. Logically, both sexes are included in the dataset because thisgroup best represents the labour population of the Western world.

Male (39.37%)

Female (60.63%)

4

3.3 Level of education 3 EXPLORATION AND SELECTION OF NORM DATA

3.3 Level of education

Because the Big Five Personality Test is specially developed for average to higher educated people, it wasdecided to select a number of education levels to be included in the dataset. The blue shaded educationlevels in the diagram are included in the dataset.

Prim

ary

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ol

Hig

h sc

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Col

lege

Uni

vers

ity

PhD

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er

020000400006000080000

100000120000140000

3.4 Nationality

The numbers of nationalities represented in the original dataset is enormous: 217 countries, dependenciesand territories were represented with more than 10 respondents.

Because the Big Five Personality Test is developed for the English speaking market of the Western world, acountry selection is made. Countries selected in the final dataset are shaded blue.

MalaysiaCanada

Australia

United Kingdom

Philippines

IndiaOther

United States

5

3.5 Labour market position 3 EXPLORATION AND SELECTION OF NORM DATA

3.5 Labour market position

The respondent was asked about his/her labour market position. Only the labour market positions Salariedemployment, Self-employed/Freelancer and Officially unemployed were used in the dataset, because thisgroup best represents the labour population of the Western world.

Otherwise

Welfare

Officially unemployed

Volunteer

Retired

Unable to work / long−term illness

Housewife / homemaker

Working as a family member of self−employed / freelancer

Self−employed / freelancer

Salaried employment

Pupil / student / trainee

0

5000

0

1000

00

1500

00

2000

00

6

3.6 Work sector/industry 3 EXPLORATION AND SELECTION OF NORM DATA

3.6 Work sector/industry

The respondent was asked to indicate in which working sector he/she works. A choice could be made fromthe 23 work sectors used in the model of EurOccupations (Wageindicator.org 2009). The distribution givesno reason to correct for this.

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7

3.7 Completion time 3 EXPLORATION AND SELECTION OF NORM DATA

3.7 Completion time

Looking at the duration of completion of a questionnaire is a good way to determine how seriously arespondent has completed the questionnaire. It was decided to take between 5 and 45 minutes in the finaldataset.

Seconds

Fre

quen

cy

0 500 1000 1500 2000 2500 3000

020

0060

0010

000

3.8 Response Variation

Looking at a respondent’s response variation is a good way to determine how seriously the respondent hascompleted the questionnaire. It was decided to only include a response variation of 5 in the final dataset. Aresponse variation of 5 means that a respondent has used all the answer options of the Likert-5 scale at leastonce over all 120 items.

1 2 3 4 5

0e+

002e

+05

4e+

05

8

3.9 Human response 3 EXPLORATION AND SELECTION OF NORM DATA

3.9 Human response

Online questionnaires can suffer from crawlers and bots who fill in the questionnaires automatically. By usinga consistency measure we can exclude responses that are not consistent from the dataset.

The consistency measure psychometric synonym (Meade and Craig 2012) has been used to identify artificialand random responses. This consistency measure is calculated by first selecting all item pairs that correlate> .60 across the entire dataset. In this dataset, 9 item pairs are selected. Next, for each respondent thepsychometric synonym score is calculated which is equal to the within-person correlation of the selected itempairs.

The cut-off value of 0.2 used by Meade & Craig (2012) was used to filter artificial responses and responseswith a random response pattern from the dataset. In the histogram below, the deleted responses are shadedgray.

−0.4 −0.15 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Within−person correlation

Fre

quen

cy

050

015

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9

4 ANALYSIS

4 Analysis

The final dataset includes 15.107 respondents.

4.1 Raw scores

In this chapter the raw scores of all the facets and factors are presented. X-axes have been omitted becauseof possible unwanted reuse of the norm data.

4.1.1 Factors

The histograms of the raw factor scores all show a normal distribution.Factor: Openness to experience

010

030

050

0

Factor: Conscientiousness

010

020

030

040

0

Factor: Extraversion

010

020

030

040

0

Factor: Agreeableness

010

030

050

0

Factor: Natural reactions

050

150

250

350

10

4.1 Raw scores 4 ANALYSIS

4.1.2 Facets

The histograms of the raw facet scores usually show a normal distribution, with sometimes a floor and/orceiling effect for scales with a high socially desirable component.

4.1.2.1 Openness to experienceFacet: Imagination

050

010

0015

00

Facet: Artistic interests

050

010

0015

00

Facet: Depth of emotions

050

010

0015

0020

00

Facet: Willingness to experiment

050

010

0015

00

Facet: Intellectual curiosity

050

010

0015

00

Facet: Tolerance for diversity

050

010

0015

0020

00

11

4.1 Raw scores 4 ANALYSIS

4.1.2.2 ConscientiousnessFacet: Sense of competence

050

015

0025

00

Facet: Orderliness

050

010

0015

00

Facet: Sense of responsibility

050

010

0015

0020

00

Facet: Achievement striving

050

010

0015

0020

00

Facet: Self−discipline

050

010

0015

00

Facet: Deliberateness

050

010

0015

00

12

4.1 Raw scores 4 ANALYSIS

4.1.2.3 ExtraversionFacet: Warmth

050

010

0015

00

Facet: Gregariousness

050

010

0015

00

Facet: Assertiveness

050

010

0015

0020

00

Facet: Activity level

050

010

0015

00

Facet: Excitement seeking

050

010

0015

00

Facet: Positive emotions

050

010

0015

0020

00

13

4.1 Raw scores 4 ANALYSIS

4.1.2.4 AgreeablenessFacet: Trust in others

050

010

0015

0020

00

Facet: Sincerity

050

015

0025

00

Facet: Altruism

050

010

0020

00

Facet: Compliance

050

010

0015

0020

00

Facet: Modesty

050

010

0015

00

Facet: Sympathy

050

010

0020

00

14

4.1 Raw scores 4 ANALYSIS

4.1.2.5 Natural reactionsFacet: Anxiety

020

060

010

00

Facet: Angry hostility

020

060

010

0014

00

Facet: Moodiness/Contentment

050

010

0015

0020

00

Facet: Self−consciousness

050

010

0015

00

Facet: Self−indulgence

050

010

0015

00

Facet: Sensitivity to stress

050

010

0015

00

15

4.2 Correlations 4 ANALYSIS

4.2 Correlations

4.2.1 Correlations between factors

The five factors generally show minimal correlations. Natural reactions shows clear negative correlations withConscientiousness and Extraversion.

O

C

E

A

N

O C E A N

Big Five Factors

Big

Fiv

e Fa

ctor

s

−0.4

−0.2

0.0

0.2

value

16

4.2 Correlations 4 ANALYSIS

4.2.2 Correlations between facets

The 30 facets generally show minimal correlations outside their own facets, and average correlations withintheir own facets.

The previously observed negative correlation between Extraversion and Natural reactions can, at the facetlevel, mainly be traced back to negative correlations between Self-consciousness (N4) and Warmth (E1), andMoodiness/Contentment (N3) and Positive emotions (E6).

These results are consistent with the scientific literature and provide a good first indication of the internalstructure.

O1

O2

O3

O4

O5

O6

C1

C2

C3

C4

C5

C6

E1

E2

E3

E4

E5

E6

A1

A2

A3

A4

A5

A6

N1

N2

N3

N4

N5

N6

O1 O2 O3 O4 O5 O6 C1 C2 C3 C4 C5 C6 E1 E2 E3 E4 E5 E6 A1 A2 A3 A4 A5 A6 N1 N2 N3 N4 N5 N6

Big Five Facets

Big

Fiv

e Fa

cets

−0.4

0.0

0.4

value

17

4.3 Reliability 4 ANALYSIS

4.3 Reliability

Cronbach’s alpha (Cronbach and Shavelson 2004) is a measure of the reliability of psychometric tests orquestionnaires. The value of alpha is an estimate for the lower limit of reliability of the test in question.

4.3.1 Factors

A often used criterion for instruments used in advisory situations is that the reliability coefficient ofCronbach’s alpha should not be lower than .60. Scores higher than .80 are assessed as ‘good’.

On average across the five factors, the reliability coefficient is 0.88, which may be considered very high.

Factors Item count Cronbach’s AlphaOpenness to experience 24 0.81564Conscientiousness 24 0.90888Extraversion 24 0.89249Agreeableness 24 0.86265Natural reactions 24 0.91921

If an item does not correlate sufficiently with the other items of the same factor, it damages the reliability ofsaid factor. Below is shown what happens to the Cronbach’s Alpha of a factor when one of the 24 items isremoved.

If item deleted O C E A N1 0.806 0.904 0.885 0.859 0.9162 0.808 0.905 0.884 0.857 0.9143 0.809 0.907 0.885 0.859 0.9144 0.809 0.906 0.887 0.857 0.9125 0.800 0.906 0.884 0.855 0.9166 0.806 0.906 0.884 0.855 0.9157 0.805 0.904 0.887 0.854 0.9178 0.804 0.905 0.885 0.858 0.9179 0.811 0.906 0.888 0.857 0.91310 0.811 0.907 0.888 0.855 0.91411 0.814 0.908 0.888 0.855 0.91312 0.814 0.905 0.888 0.855 0.91613 0.814 0.905 0.889 0.861 0.91714 0.811 0.906 0.890 0.859 0.91815 0.814 0.904 0.890 0.853 0.91916 0.807 0.907 0.894 0.856 0.91917 0.807 0.904 0.889 0.856 0.91818 0.806 0.904 0.889 0.866 0.92019 0.806 0.903 0.898 0.865 0.92020 0.805 0.905 0.897 0.861 0.91821 0.809 0.907 0.887 0.859 0.91322 0.817 0.905 0.886 0.857 0.91423 0.813 0.905 0.888 0.855 0.91424 0.812 0.903 0.888 0.857 0.916

18

4.4 Construct Validity: Factor Analysis 4 ANALYSIS

If item deleted O C E A N

4.3.2 Facets

The average Cronbach’s Alpha of the 30 facets is 0.753, which is a good performance considering the lengthof the scales.

Factors Facets Item count Cronbach’s AlphaOpenness to experience Facet: Imagination 4 0.77087

Facet: Artistic interests 4 0.73223Facet: Depth of emotions 4 0.65264Facet: Willingness to experiment 4 0.66261Facet: Intellectual curiosity 4 0.69076Facet: Tolerance for diversity 4 0.49487

Conscientiousness Facet: Sense of competence 4 0.72658Facet: Orderliness 4 0.83003Facet: Sense of responsibility 4 0.69670Facet: Achievement striving 4 0.75943Facet: Self-discipline 4 0.74301Facet: Deliberateness 4 0.86425

Extraversion Facet: Warmth 4 0.80829Facet: Gregariousness 4 0.81566Facet: Assertiveness 4 0.86924Facet: Activity level 4 0.71323Facet: Excitement seeking 4 0.65720Facet: Positive emotions 4 0.81739

Agreeableness Facet: Trust in others 4 0.84768Facet: Sincerity 4 0.74710Facet: Altruism 4 0.73092Facet: Compliance 4 0.66146Facet: Modesty 4 0.73948Facet: Sympathy 4 0.72971

Natural reactions Facet: Anxiety 4 0.82595Facet: Angry hostility 4 0.86720Facet: Moodiness/Contentment 4 0.85947Facet: Self-consciousness 4 0.70584Facet: Self-indulgence 4 0.76216Facet: Sensitivity to stress 4 0.79752

4.4 Construct Validity: Factor Analysis

4.4.1 Screeplot

In factor analysis, a screeplot or eigenvalue diagram is a graph in which the eigenvalues of the possiblevariables for the factors are plotted in order of decreasing magnitude.

In the table below you can see that there are 5 clear components (PC) with an eigenvalue > 1.0. Thiscorresponds with well-known scientific literature which states that personality contains 5 components.

19

4.4 Construct Validity: Factor Analysis 4 ANALYSIS

0 5 10 15 20 25 30

02

46

Scree plot of 30 Big Five facets

component number

Eig

en v

alue

s of

com

pone

nts

20

4.4 Construct Validity: Factor Analysis 4 ANALYSIS

4.4.2 Principal Components Analysis

Principal component analysis is a multivariate method of analysis in statistics to describe a large amount ofdata with a smaller number of relevant quantities, the main components or principal components.

The table below shows the results of a PCA with varimax rotation. The 30 facets can clearly be reduced tothe five components to which they belong according to the theoretical model of the Big Five. The dominantfactor Extraversion attracts a lot of variance, especially in the form of negative charges of Natural reactions.There are only a number of facets that have a higher primary charge on another component.

All in all, the analysis shows a very recognizable and satisfactory picture.

Factor Code Facet RC1 RC2 RC3 RC4 RC5Extraversion E1 Facet: Warmth 0.828

E2 Facet: Gregariousness 0.832E3 Facet: Assertiveness 0.481 0.513E4 Facet: Activity level 0.436 0.493E5 Facet: Excitement seeking 0.574E6 Facet: Positive emotions 0.69

Conscientiousness C1 Facet: Sense of competence 0.801C2 Facet: Orderliness 0.578C3 Facet: Sense of responsibility 0.529C4 Facet: Achievement striving 0.727C5 Facet: Self-discipline 0.784C6 Facet: Deliberateness 0.432 -0.574

Agreeableness A1 Facet: Trust in others 0.454 0.407A2 Facet: Sincerity 0.65A3 Facet: Altruism 0.793A4 Facet: Compliance 0.595A5 Facet: Modesty 0.556A6 Facet: Sympathy 0.706

Natural reactions N1 Facet: Anxiety 0.69N2 Facet: Angry hostility 0.708N3 Facet: Moodiness/Contentment 0.552N4 Facet: Self-consciousness -0.729N5 Facet: Self-indulgence 0.503N6 Facet: Sensitivity to stress 0.638

Openness to experience O1 Facet: Imagination 0.587O2 Facet: Artistic interests 0.689O3 Facet: Depth of emotions 0.701O4 Facet: Willingness to experiment 0.543O5 Facet: Intellectual curiosity 0.746O6 Facet: Tolerance for diversity 0.595

21

5.2 Group differences 5 NORMS

5 Norms

5.1 Labour force

For a correct norm group, the dataset must properly reflect the intended group of users, in this case theWestern world labour force. Because a dataset almost never has the same composition as the intended usergroup, weighing is used.

The dataset is weighted according to the distribution in the table below.

Criterium Groepen PopulationSexe Female 50,50%

Male 49,50%Education Average education 62,60%

Higher education 37,40%Age 15-24 17,40%

25-44 45,50%45-64 37,10%

A much used standard for norm groups for use in ‘advisory’ situations is that the norm group should consistof >200 respondents. For recruitment and selection purposes this is >400. In this dataset 15107respondents are included and therefore very clearly meets this standard.

5.2 Group differences

If there are significant differences between relevant groups within a norm group, this could and should becorrected by using separate norm groups.

For comparing group averages and determining effect size, Cohen’s d is used (Cohen 1992). Effect sizes closeto zero are small, effect sizes larger than 0.8 or smaller than -0.8 are often considered large.

22

5.2 Group differences 5 NORMS

5.2.1 Sex

To determine whether norms are needed for specific groups, group differences between the sexes have beenexamined. The results of these analyses are shown below.

0.00

0.01

0.02

0.03

Openness to experience

Female

Male

0.00

0.01

0.02

Conscientiousness

Female

Male

0.00

0.01

0.02

Extraversion

Female

Male

0.00

0.01

0.02

0.03

Agreeableness

Female

Male

0.000

0.005

0.010

0.015

0.020

Natural reactions

Female

Male

Effect size

Factor Cohen’s D (Male-Female)Openness to experience 0.128Conscientiousness 0.121Extraversion -0.024Agreeableness 0.543Natural reactions 0.252

Given that no impact sizes of -0.8 or 0.8 have been found, it can be concluded that the use of a single normfor the sexes is justified.

23

5.2 Group differences 5 NORMS

5.2.2 Age

In order to determine whether norms are needed for specific groups, group differences between age groupswere taken into account. The results of these analyses are shown below.

0.00

0.01

0.02

0.03

Openness to experience

18 − 24

25 − 44

45 − 67

0.00

0.01

0.02

0.03

Conscientiousness

18 − 24

25 − 44

45 − 67

0.000

0.005

0.010

0.015

0.020

0.025

Extraversion

18 − 24

25 − 44

45 − 67

0.00

0.01

0.02

0.03

Agreeableness

18 − 24

25 − 44

45 − 67

0.000

0.005

0.010

0.015

0.020

Natural reactions

18 − 24

25 − 44

45 − 67

Effect size

Factor Cohen’s D (Older-Younger)Openness to experience 0.156Conscientiousness -0.724Extraversion -0.151Agreeableness -0.552Natural reactions 0.705

Given that no impact sizes of -0.8 or 0.8 have been found, it can be concluded that the use of a single normfor age is justified.

24

5.2 Group differences 5 NORMS

5.2.3 Level of education

In order to determine whether standards are needed for specific groups, group differences between educationlevels have been examined. The results of these analyses are shown below.

0.00

0.01

0.02

0.03

Openness to experience

Highschool

College

University

0.00

0.01

0.02

0.03

Conscientiousness

Highschool

College

University

0.00

0.01

0.02

Extraversion

Highschool

College

University

0.00

0.01

0.02

0.03

Agreeableness

Highschool

College

University

0.000

0.005

0.010

0.015

0.020

Natural reactions

Highschool

College

University

Effect size

Factor Cohen’s D (University-Highschool)Openness to experience -0.330Conscientiousness -0.410Extraversion -0.242Agreeableness -0.312Natural reactions 0.334

Given that no impact sizes of -0.8 or 0.8 have been found, it can be concluded that the use of a singlestandard for education level is justified.

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6 CONCLUSION

6 Conclusion

The results of this study show that the Big Five Personality Test of 123test is a reliable and valid instrumentwith a solid norm to be used among Western world respondents with an average to higher educational level,with an age between 18 and 67 years for self-analysis, in career guidance or in other professional settings.

Reliability

The results of this study show that the Big Five Personality Test of 123test scores well to very well on thereliability coefficients commonly used in science.

Validity

The results of this study show that the Big Five Personality Test of 123test shows good construct validity ofthe measured constructs.

Norms

The results of this study show that the Big Five Personality Test of 123test has a good norm that shows nodifferences between groups.

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7 REFERENCES

7 References

Cohen, Jacob. 1992. “A Power Primer.” Psychological Bulletin 112 (1). American Psychological Association:155.

Cronbach, Lee J., and Richard J. Shavelson. 2004. “My Current Thoughts on Coefficient Alpha andSuccessor Procedures.” Educational and Psychological Measurement 64 (3): 391–418.

Meade, Adam W, and S Bartholomew Craig. 2012. “Identifying Careless Responses in Survey Data.”Psychological Methods 17 (3). American Psychological Association: 437.

Wageindicator.org. 2009. “EurOccupations.”

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